class_docstr: Linearly spaced clipping.
  
  `n_samples` are generated with clipping spaced linearly
  between `lower` and `upper`.
  
  This transformation affects the following attributes:
  - Audio
  
  Attributes
  ----------
  n_samples : int > 0
      Number of deformations to generate
  
  lower : float > 0.0
  upper : float in (lower, 1.0)
      Minimum and maximum bounds on the clip parameters
  
  See Also
  --------
  Clipping
  RandomClipping
class_docstr_tok: ["Linearly","spaced","clipping",".","`","n_samples","`","are","generated","with","clipping","spaced","linearly","between","`","lower","`","and","`","upper","`",".","This","transformation","affects","the","following","attributes",":","-","Audio","Attributes","--","--","--","--","--","n_samples",":","int",">","0","Number","of","deformations","to","generate","lower",":","float",">","0.0","upper",":","float","in","(","lower",",","1.0",")","Minimum","and","maximum","bounds","on","the","clip","parameters","See","Also","--","--","--","--","Clipping","RandomClipping"]
code: def __init__(self, n_samples=3, lower=0.4, upper=0.8):
      AbstractClipping.__init__(self)
  
      if n_samples <= 0:
          raise ValueError("n_samples must be strictly positive.")
  
      if lower <= 0.0 or lower >= 1.0:
          raise ValueError("lower parameter domain is strictly (0,1).")
  
      if upper <= lower:
          raise ValueError("upper must be strictly larger than lower.")
  
      if upper >= 1.0:
          raise ValueError("upper parameter domain is strictly (0,1).")
  
      self.n_samples = n_samples
      self.lower = float(lower)
      self.upper = float(upper)
  
file_path: muda/muda/deformers/clipping.py
imports: import numpy
  from ..base import BaseTransformer, _get_rng
label_desc: Use image node_id 1 to create a new LinearClipping object from inherited base classes: AbstractClipping with example: obj = LinearClipping(n_samples, lower, upper)